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PoseTrack: A Benchmark for Human Pose Estimation and Tracking

CVPR 2018 · 3D vision › Human pose estimation · approach: Temporal video pose estimation · 516 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
combine MPII and PoseTrack training data instead of using MPII alone (ablation)ArtTrack-baseline · PoseTrack testmAP: 66.4 vs. 68.7Helped
add MPII training data to PoseTrack-only training (ablation)ArtTrack-baseline · PoseTrack testmAP: 55.5 vs. 68.7Helped
use MPII only rather than MPII plus PoseTrack (ablation)ArtTrack-baseline · PoseTrack testmAP: 68.7 vs. 66.4Hurt
raise the part detection cutoff threshold from τ=0.1 to τ=0.8 (ablation)ArtTrack-baseline · PoseTrack testMOTA: 38.1 to 53.4Helped
raise the part detection cutoff threshold from τ=0.1 to τ=0.8 (ablation)ArtTrack-baseline · PoseTrack testmAP: 68.6 -> 62.1Hurt
use a higher detection threshold to tune tracking (ablation)ArtTrack-baseline · PoseTrack testmAP: 68.6 -> 62.1Mixed
use the modified PAF pose model rather than PAF in ML-LABPoseTrack tracking · PoseTrack testmAP: 62.5 vs. 70.3Helped
use simple frame-to-frame association on hard sequences with fast camera motioncurrent pose tracking approaches · PoseTrack testMOTAHurt
evaluate on more complex, highly articulated sequencescurrent pose estimation and tracking methods · PoseTrack testmAPHurt

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